High Level Architectural Design

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High Level Architecture v3

Overall Architecture

aHuman architecture is based on biological model. See BiologicalLifeResearch.

aHuman framework contains below high-level modules:

Module	Name	Implements	Notes
ACTUATORS	aWee, aYou, aPets, aHuman	set of Sensors and Effectors for different targets	Can interact with external world; there can be different sets of actuators, aPets or aWee can be test/trial one for prototyping aHuman
MIND	mind	intelligence and feeling	in biological model includes brain, feeling components and all tracts
PLATFORM	platform	generic software application functions	contains library components shared across different aHuman executable targets or specific auxiliary executable targets
TOOLS	tools	generic auxiliary executables	using platform and libraries, multiple utilities combined in one executable, like cvs/svn/p4
LIBRARY	library	ready-to-use libraries	can be project-generated library or external library; other modules are using only binaries and headers

Possible executables:

Resulting high-level architecture model is below one:

Mind is a sole part of aHuman and has its own layered HLA:

See MindArchitecture
mind component hierarchy can be sliced to present optimal level of functional items - set of mind regions
consider all the mind regions and links as produced by specific set of mind services, where one service is aimed to create specific set of circuit patterns in order to implement specific mental function
mind area is a group of mind regions and their links which are produced by specific mind service
neurolink implements excitatory and inhibitory neurotransmitters and neuromodulators, resulting in different timing and mode of signal propagation

Layer	Key Components	Implements
mind target	MindTarget, MindSensor, MindEffector	Mind target framework and lifecycle. Provides facade interfaces to specific target, isolates of mind and platform design
mind service	MindService, MindNet	High-level mind construction and service lifecycle implementation, factory for specific mind components, establish and maintain wide neural networks. Handles mind birth, growth and wake/sleep cycle
mind area	MindArea and specific areas in unified aHuman mind	Lifecycle of mind areas, regional nature and structure of unified mind. Sole mind architecture based on underlying technologies of next layers
mind region	MindRegion and specific region types of cytoarchitecture	Connectivity architecture of specific mind tissue types - cortex, nuclei and nerve system. Based on low-level neural components
mind matter	NeuroSignal, NeuroPool, NeuroLink and specific implementations according to neurotransmitter systems	Neuron types, neural networks, neurotransmitters. Effective representation of low-level mind state
auxiliary mind objects	MindAreaLink, MindRegionLink, MindActiveMemory, MindMessage, MindSpace, MindLocation, MindMap	Principal mind connectivity and thought flow. Effective execution of network signal propagation. Geometry of the mind - one more real dimension in brain modeling

Platform contains multiple services and split on HLA level to components as well:

Component	Main Class	Implements	Depends on
services	ServiceManager	base types, plain and template classes, logging, exception handling, service and platform lifecycle	-
environment	EnvService	Handling environment, including configuring, deployed instance, operating system and other available resources	-
objects	ObjectService	Unified objects and object persistence, object types	-
math	MathService	Specific mathematical calculations	-
persisting	PersistingService	Persistence of memory in filesystem space	objects
threads	ThreadService	multi-threading environment, CPU management, performance monitoring	objects
messaging	MessagingService	Internal decoupling mechanism to arrange manageable numerous interactions among components	objects, threads
media	MediaService	Communicating with external world using standard protocols with non-aHuman services or internal aHuman protocol with aHuman tools	objects, threads, messages
scheduling	SchedulerService	Running configured tasks regularly	objects, threads, messages
statistics	StatService	Collecting performance statistics	objects, scheduling, messages
adminapi	AdminApi	API to access internal aHuman components and run tests or specific operations	media
testing	TestService	Testing and debugging support	objects, messaging

Layer/Module	Design Rate	Code Rate	Advances and TBD
ACTUATORS	2 focus	2 focus	adv: unified sensor model, filesyswalker, tbd: overt and covert attention, digital-neural protocol
MIND
mind target	2	1 focus	adv: separated by design, tbd: full lifecycle implementation in HLAv3
mind service	3	1 focus	adv: biomodel, tbd: layered architecture, transition to HLAv3
mind area	2 focus	1	adv: cortex types, tbd: neocortex area design
mind region	2 focus	1 focus	adv: HTM, reqs, features, tbd: complete neocortex region model
mind matter	2	2 focus	adv: sourceforge cortex, fann, tbd: trial neocortex implementation
mind space	1	1	adv: locations, tbd: -
mind stream	2 focus	2 focus	adv: -, tbd: link hierarchy, HTM links
PLATFORM	3	3	adv: life cycle, component-based code, HLAv3 codebase, tbd: -
TOOLS	2	2	adv: aiconsole, tbd: -
LIBRARY	2	2	adv: opencortex, fann, genrtn, chat, opencv tbd: -

Feasibility Study gives us below limitations for aHuman design:

don't try to replicate human design exactly
use high-quality model of NN with 1Kb memory per neuron
use NNs for personality areas and knowlets for others
use 500K neurons in operating memory (0.05% of HI)
use 30K focused neurons (6% of AI NNs)
develop knowlets as plug-ins
- use up to 500M in operating memory for activated knowlets (0.5% of HI)
- remaining inactive knowlets can use up to 100Gb disk space
separate feeling from emotions
treat feeling as internal motivation system
- use broken Markov network as a "pain" body signal
- use sensor data consumed to adjust Markov network as "feed" body signal
- use expected action in Markov network as internal motivator
- curiosity is exploration action in Markov network
- derive new "uncertainty" body signal as mismatch between expected/suggested action and actual action
don't create Markov networks initially, create HMM factories to construct networks
create learning cycle based on body signals
- construct body reward function from body signals
- create system of feedback recognition, train it using body reward function
- create feedback reward function as output of feedback recognition system
- use feedback reward function to learn all active Markov networks

mind consists of mind areas
mind area consists of neural networks of various types
single AI component is responsible for specific area and defines how it is constructed from networks - shape, used network types, sizes and inter-area links
brain holds mind
brain defines how areas are connected
brain contains thread pool, executing elementary operations
elementary operation is either network input/output calculation or link walkthough
any operation leads to learning - either inside one network, or inside link
networks can be of small typical size - e.g. 300 neurons, and thread pool can be 100 threads executing their operations and then sleeping for a while
networks porcessed in the same time mean "mind focus"
after processing focus is changed